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Quality control is one of the three cyclical quality assurance activities that help keep a system under statistical control. Typical quality control activities include creating quality control charts, conducting proficiency testing, and documenting and archiving results.
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Automated quality control for within and between studies diffusion MRI data using a non-parametric framework for

Matteo Bastiani1, Michiel Cottaar2, Sean P Fitzgibbon2

  • 1Wellcome Centre for Integrative Neuroimaging - Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, UK; Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, UK.

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Summary

Automated quality control (QC) for diffusion MRI (dMRI) is crucial for large studies. This work presents a new framework using FSL EDDY for artifact detection and correction, improving data analysis reliability.

Keywords:
Diffusion MRIEddy currentMovementQuality controlSusceptibility

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Area of Science:

  • Neuroimaging
  • Medical Physics
  • Biomedical Engineering

Background:

  • Diffusion MRI (dMRI) data are susceptible to artifacts from hardware and subjects.
  • These artifacts can negatively impact downstream data analysis.
  • Visual quality control (QC) is impractical for large population studies, necessitating automated methods.

Purpose of the Study:

  • To introduce an automated diffusion MRI QC framework for both single-subject and group-level analyses.
  • To provide tools for reliable artifact detection and correction in dMRI data.
  • To facilitate cross-study harmonization efforts in large-scale dMRI research.

Main Methods:

  • Utilized FSL EDDY, a comprehensive, non-parametric approach for movement and distortion correction.
  • Extracted a rich set of QC metrics sensitive and specific to various artifact types.
  • Developed two distinct tools: QUAD for single-subject QC and SQUAD for group-wise QC.

Main Results:

  • The framework successfully identifies and quantifies artifacts in diffusion MRI data.
  • The extracted QC metrics demonstrate sensitivity to different types of artifacts.
  • The developed tools provide practical solutions for automated QC in dMRI.

Conclusions:

  • The proposed automated QC framework enhances the reliability of diffusion MRI analyses.
  • QUAD and SQUAD offer valuable tools for single-subject and group-level dMRI quality assessment.
  • This approach is essential for ensuring data integrity in large population studies and aids in cross-study harmonization.